Different action user-interface components in a comparison view

ABSTRACT

Different action user-interface components in a comparison view are described. Initially, a selection is received to display a comparison view via a user interface of a listing platform. Multiple listings of the listing platform are selected for inclusion in the comparison view. A comparison view system determines which action of a plurality of actions, used by the listing platform, to associate with each of the listings. A display device displays the multiple listings concurrently in a comparison view via a user interface of the listing platform and also displays an action user-interface component (e.g., a button) in each of the plurality of listings. The action user-interface component is selectable to initiate the action associated with the respective listing. In accordance with the described techniques, the action user-interface component displayed in at least two of the multiple listings is selectable to initiate different actions in relation to the respective listing.

BACKGROUND

Service provider systems continue to make advances in computingtechnologies which enable various listings to be surfaced to clientdevices. With these advances, a continually increasing number of serviceproviders and associated applications surface listings to client devicesfor various listed “items.” Example items listed by these serviceproviders include products and services such as consumer products (newand used), financial instruments, real property, property rentals,service offerings (e.g., house cleaning, babysitting, landscaping,etc.), classified offerings, and so on. Other examples of listed itemsmay include profiles, such as player profiles in fantasy sports, userprofiles in online dating, user profiles in social networking, and soforth. In addition to advances in technologies for surfacing listings,service provider systems also continue to make advances in computingtechnologies for comparing these listings.

Conventional techniques for comparing these listings typically involve aservice provider system selecting a group of multiple listings based onone or more criteria, such as the listings being similar (e.g., to alisting being viewed currently), the listings being included in a listof listings (e.g., a watch or wish list), the listings having been addedto an online shopping cart for potential purchase, the listings havingbeen bid on in an online auction, and so forth. These service providersthen display the listings in a comparison view, where the listings areoften displayed side-by-side (or stacked) with a plurality of attributessuch that each listing's information for a given attribute (e.g., price)is adjacent to neighboring listings' information for the givenattribute. In a scenario where listings of the comparison view arearranged horizontally, one listing to another, the listings may beconsidered columns of the comparison view and the attributes may beconsidered rows of the comparison view. To this extent, users may simplybe able to scan and/or scroll left and right across the comparison viewto view information for a given attribute, e.g., a user may be able tovisually scan the comparison view left to right to compare an image or aprice of each listing.

However, conventional systems limit conversion achieved via comparisonviews. Some comparison views displayed by conventional systems, forinstance, display no user interface components (e.g., buttons) thatenable users to initiate an action in relation to the comparison view'slistings, while other comparison views display a same user interfacecomponent (e.g., an “Add to Cart” button) in all of the listings.Because users often leverage comparison views when trying to decidebetween listings, conventional comparison views fail to take advantageof actual engagement by users with service providers to causeconversion. Indeed, users may analyze a comparison view displayed by oneservice provider system, but then navigate to a user interface ofanother service provider system to finalize conversion, e.g., purchase alisted item, initiate contact with a profiled user, and so forth.

SUMMARY

To overcome these problems, different action user-interface componentsare leveraged in a comparison view. Initially, a selection is receivedto display a comparison view, e.g., an input is received to navigate toa web page of a particular item listed by a listing platform where thecomparison view is to be included as a component of the web page.Multiple listings of the listing platform are selected for inclusion inthe comparison view. Once the listings are selected, a comparison viewsystem determines which action of a plurality of actions, available foruse in connection with the listing platform, to associate with each ofthe listings. By way of example, the comparison view system determinesthe action to associate with each individual listing using a machinelearning model, e.g., a reinforcement learning model.

A display device displays the multiple listings concurrently in acomparison view via a user interface of the listing platform.Additionally, the display device displays an action user-interfacecomponent (e.g., a button) in each of the plurality of listings that isselectable to initiate the action associated with the respectivelisting. In accordance with the described techniques, the actionuser-interface component displayed in at least two of the multiplelistings is selectable to initiate different actions in relation to therespective listings. In order to differentiate between different actionsinitiated, the action user-interface component may, for the differentactions, be configured with differing visual characteristics, such asdifferent text (e.g., indicative of the action initiated), colors, andso forth.

This Summary introduces a selection of concepts in a simplified formthat are further described below in the Detailed Description. As such,this Summary is not intended to identify essential features of theclaimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures.

FIG. 1 is an illustration of an environment in an example implementationthat is operable to employ techniques described herein.

FIG. 2 depicts an example of a user interface displaying a comparisonview that includes multiple listings with different actionuser-interface components.

FIG. 3 depicts another example implementation of a user interface inwhich the comparison view is included as a component of the userinterface.

FIG. 4 depicts an example implementation in which a comparison viewsystem of FIG. 1 generates a comparison view with different actionuser-interface components.

FIG. 5 depicts a different example of a user interface displaying thecomparison view having multiple listings with different actionuser-interface components.

FIG. 6 depicts a procedure in an example implementation in which acomparison view is displayed with different action user-interfacecomponents.

FIG. 7 depicts a procedure in an example implementation in which acomparison view is generated, based on user data and using machinelearning, to include different action user-interface components.

FIG. 8 illustrates an example system including various components of anexample device that can be implemented as any type of computing deviceas described and/or utilized with reference to FIGS. 1-7 to implementembodiments of the techniques described herein.

DETAILED DESCRIPTION

Overview

Service provider systems continue to make advances in computingtechnologies which enable various listings to be surfaced to clientdevices. Conventional techniques for comparing these listings typicallyinvolve a service provider system selecting a group of multiple listingsand displaying the listings in a comparison view, e.g., where thelistings are displayed side-by-side (or stacked) with a plurality ofattributes. However, conventional systems limit conversion achieved viacomparison views. Some comparison views displayed by conventionalsystems, for instance, display no user interface components (e.g.,buttons) that enable users to initiate an action in relation to thecomparison view's listings, while other comparison views display a sameuser interface component (e.g., an “Add to Cart” button) in all of thelistings. Because users often leverage comparison views when trying todecide between listings, conventional comparison views fail to takeadvantage of actual engagement by users with service providers to causeconversion.

To overcome these problems, different action user-interface componentsare leveraged in a comparison view. Initially, a selection is receivedto display a comparison view. By way of example, an input is received tonavigate to a web page of a particular item listed by a listingplatform, where the comparison view is to be included as a component ofthe web page. Alternately or in addition, a user input may be receivedspecifically to display the comparison view, such as by receivingselections (e.g., via “Select to Compare” user-interface components) toadd listings to a list and receiving a selection to view those listings.Responsive to this, a comparison view system determines which listingslisted by the listing platform to include in the comparison view. Thecomparison view system may determine which listings to include based onone or more criteria, such as the listings being similar (e.g., to alisting being viewed currently), the listings being included in a listof listings (e.g., a watch or wish list), the listings having been addedto an online shopping cart for potential purchase, the listings havingbeen bid on in an online auction, and so forth.

In contrast to conventional systems—which include no actionuser-interface component in the listings of a comparison view or includea same action user-interface component (e.g., an “Add to Cart” button)in all listings of a comparison view—the comparison view systemdetermines which action of a plurality of actions, available for use inconnection with the listing platform, to associate with each of thelistings. In this way, the listings in the described comparison view mayinclude action user-interface components that are selectable to initiatedifferent actions. For instance, the action user interface component ina first listing of the comparison view may be selectable to initiate an“Add to Cart” action while the action user interface component in asecond listing of the comparison view is selectable to initiate an “Addto Watch List” action, but not the “Add to Cart” action.

In one or more implementations, the comparison view system determineswhich available action to associate with a listing of the comparisonview using machine learning. By way of example, the comparison viewsystem uses a reinforcement learning model to determine which availableaction to associate with a listing. By using reinforcement learning, thecomparison view system is able to account for a real-time context inwhich the comparison view is to be displayed. The context describes avariety of aspects relating to the comparison view and at a particulartime the comparison view is to be displayed. Among other aspects, forinstance, the context describes behavior of a user to which thecomparison view is displayed, behavior of other users of the listingsystem in relation to the listings in the comparison view, and alsoaccounts for behavior changes of the user and the other users over time.Given this, the reinforcement learning model may predict, for a sameuser, a first set of actions for listings at a first time and a second,different set of actions for those same listings at a second, subsequenttime. Similarly, the reinforcement learning model may predict, for afirst user, a first set of actions for listings at a particular timeand, for a second user, a second, different set of actions for the samelistings at the particular time. Use of the reinforcement model enablesthe comparison view system to determine actions to associate with thelistings of a comparison view in a way that optimizes the displayedcombinations of action user-interface components for achieving someobjective, e.g., increasing a gross amount of money users spend with thelisting platform, increasing a number of users converting (e.g., makinga purchase or initiating contact with a profiled user) with the listingplatform, and so forth.

After the actions are determined for the listings, the comparison viewcan be displayed. A display module causes a display device to displaythe multiple listings concurrently in the comparison view via a userinterface of the listing platform. The display module also causes thedisplay device to display an action user-interface component (e.g., abutton) in each of the plurality of listings that is selectable toinitiate the action associated with the respective listing. Inaccordance with the described techniques, the action user-interfacecomponent displayed in at least two of the multiple listings isselectable to initiate different actions in relation to the respectivelisting. In order to differentiate between different actions initiated,the action user-interface component may, for the different actions, beconfigured with differing visual characteristics, such as different text(e.g., indicative of the action initiated), colors, and so forth. Thecomparison view described herein is an improvement over conventionalcomparison views because there is typically a limited amount of screenspace available for each listing in a comparison view—the limited amountof space limiting the listing to a single interface component—andbecause the ability to display different action user-interfacecomponents can take advantage of how users will actually interact withthe different listings. Additionally, the ability to display differentaction user-interface components can cause higher conversion ratestoward achieving a goal, such as increasing the listing platform'sabove-discussed objectives.

In the following discussion, an example environment is first describedthat may employ the techniques described herein. Example implementationdetails and procedures are then described which may be performed in theexample environment as well as other environments. Consequently,performance of the example procedures is not limited to the exampleenvironment and the example environment is not limited to performance ofthe example procedures.

Example Environment

FIG. 1 is an illustration of a digital medium environment 100 in anexample implementation that is operable to employ different actionuser-interface components in a comparison view as described herein. Theillustrated environment 100 includes a computing device 102, a listingplatform 104, and additional computing devices 106. The computing device102, the listing platform 104, and the additional computing devices 106are communicatively coupled, one to another, via a network 108.

Devices that are usable to implement the computing device 102, thelisting platform 104, and the additional computing devices 106 may beconfigured in a variety of ways. A suitable device, for instance, may beconfigured as a desktop computer, a laptop computer, a mobile device(e.g., assuming a handheld configuration such as a tablet or mobilephone), a wearable device, one or more server devices, and so forth.Thus, suitable devices may range from full resource devices withsubstantial memory and processor resources (e.g., personal computers,game consoles) to low-resource devices with limited memory and/orprocessing resources (e.g., wearable devices). Additionally, thesedevices may be representative of a plurality of different devices, suchas multiple servers utilized by a business to perform operations “overthe cloud” as further described in relation to FIG. 8.

The computing device 102 is illustrated as including applications 110and display module 112. The applications 110 represent various computerapplications of the computing device 102 which provide a variety offunctionality. One example of an application 110 is a browser, whichenables a user 114 of the computing device 102 to access informationover the network 108 (e.g., the Internet), such as by navigating towebpages provided by various service providers. Other examples of theapplications 110 include mobile applications, such as e-commerceapplications, fantasy sports applications, financial instrument tradingapplications, social networking applications, online datingapplications, and so forth. It is to be appreciated that theapplications 110 may include a variety of computer applications on orotherwise accessible to the computing device 102 without departing fromthe spirit or scope of the described techniques.

Broadly speaking, the display module 112 causes display of digitalvisual content via a display device 116 associated with the computingdevice 102. In the illustrated environment 100, the display device 116is depicted incorporated within a housing of the computing device 102,however; it is to be appreciated that in various implementations thedisplay device 116 may not be incorporated within a housing of thecomputing device 102, but instead may be communicably coupled to thecomputing device 102, such as by a wired or wireless communicablecoupling.

In accordance with the described techniques, the display module 112causes display via the display device 116 of a comparison view 118. Asdiscussed in further detail in relation to FIG. 2, the comparison view118 includes a plurality of listings, each of the listings includes anaction user-interface component (e.g., a button), and the actionuser-interface components of at least two of the listings are selectableto initiate different actions in relation to the respective listing. Forexample, one or more of the listings in the comparison view 118 mayinclude an action user-interface component, such as an “Add to Cart”button, and one or more different listings of the comparison view 118may include a different action user-interface component, such as a “BuyIt Now” button, a “Place Bid” button, and so on. This configuration ofthe comparison view 118 contrasts with conventional techniques whichdisplay a same action user-interface component for each listing (e.g.,all listings have only an “Add to Cart” button) and also contrasts withconventional techniques which do not display an action user-interfacecomponent with any of the listings.

Comparison view system 120 is configured to determine which actionuser-interface components to include with each listing in the comparisonview 118. The comparison view system 120 determines which actionuser-interface components to include based on data describing behaviorthe user 114 as well as data describing behavior of other users 122 ofthe additional computing devices 106. The illustrated environment 100includes user data 124, which is illustrated as being stored in storage126 and includes tracked interactions 128. The tracked interactions 128correspond to data describing interactions of the user 114 and the otherusers 122 with the listing platform 104, such as interactions withvarious user interfaces exposed to the user 114 and the other users 122by the listing platform 104.

These interactions may be tracked by a tracking service (not shown) ofthe listing platform 104 and describe interactions of users, forinstance, with a home page of the listing platform 104, a searchcomponent of the listing platform 104, a view item page of the listingplatform 104, a checkout page of the listing platform 104, and so forth.Examples of the data that may be collected to track these interactionsand produce the tracked interactions 128 include clickstream data, gazetracking data, voice command data, and so on. Additionally, it is to beappreciated that the tracked interactions 128 may describe interactionsof users with one or more homepages (e.g., when the listing platform 104configures its home page differently in different scenarios), one ormore search components (e.g., a search bar on a website, a search barintegrated within a browser, a search initiated with a voice assistantplatform, and so on), one or more view item pages (e.g., for differentlistings), one or more checkout pages for different listings and atdifferent stages of a checkout process, and so forth.

It is also to be appreciated that the tracked interactions 128 maydescribe interactions of users with the listing platform 104 viamultiple different channels, such as interactions with a web sitepresented via a browser, interactions with a mobile application,interactions to purchase items at a physical store, and so forth. Thetracked interactions 128 may describe a variety of interactions of theuser 114 and the other users 122 with the listing platform 104 withoutdeparting from the spirit and scope of the described techniques. Theuser data 124 is illustrated with ellipses to indicate that additionaldata about the user 114 and the other users 122 is stored in the storage126. This additional data can include user profile data, such as accountinformation, demographics, previous purchase history, and so forth. Theuser data 124 may include a variety of additional data about the user114 and the other users 122.

The comparison view system 120 also determines which actionuser-interface components to include based on data describing thelistings that are included in the comparison view 118. The illustratedenvironment 100 includes listing data 130, which is illustrated as beingstored in storage 132 and includes listing 134. The listing data 130 isincluded with ellipses to indicate that it includes data describingmultiple listings 134, e.g., for each listing listed by the listingplatform 104. By way of example, the listing data 130 can describemultiple products or services listed as being available for purchase viathe listing platform 104. In relation to the comparison view 118, thecomparison view system 120 leverages the listings 134 that are selectedfor the comparison view 118 to further determine which actionuser-interface component to display with each of the selected listings.

By displaying different action user-interface components for differentlistings, the comparison view 118 is an improvement over conventionallyconfigured comparison views that display a same action user-interfacecomponent with every listing in the views and over comparison views withno action user-interface components. In particular, the comparison view118 described herein is an improvement because there is typically alimited amount of screen space available for each listing in acomparison view—the limited amount of space limiting the listing to asingle interface component—and because the ability to display differentaction user-interface components can take advantage of how users willinteract in relation to different listings. By way of example, theability to display different action user-interface components can causehigher conversion rates toward achieving a goal, such as increasing agross amount of money users spend with the listing platform 104,increasing a number of users making purchases with the listing platform104, and so forth.

Having considered an example environment, consider now a discussion ofsome example details of the techniques for different actionuser-interface components in a comparison view in accordance with one ormore implementations.

Different Action User-Interface Components in a Comparison View

FIG. 2 depicts an example 200 of a user interface in accordance with thedescribed techniques. In particular, the illustrated example 200 depictsthe comparison view 118, which includes multiple listings with differentaction user-interface components.

In this example 200, the comparison view 118 includes multiple listings,specifically listings 202, 204, 206, 208, 210. The listings 202, 204,206, 208, 210 each include an action user-interface component 212, 214,216, 218, 220, respectively. In contrast to conventionally configuredcomparison views, the listings 202, 204, 206, 208, 210 of the comparisonview 118 include different action user-interface components. In theillustrated example 200, for instance, the action user-interfacecomponent 212 of the listing 202 is different from the actionuser-interface component 214 of the listing 204. The actionuser-interface component 212 of the listing 202 is also different fromthe action user-interface components 216, 218, 220 of the listings 206,208, 210, respectively. It is to be appreciated that multiple listingsin the comparison view 118 may include a same action user-interfacecomponent, such as the listings 204, 210 that include actionuser-interface components 214, 220, both of which are selectable toinitiate the action to “Buy It Now” in relation to their listing. Inaccordance with the described techniques though, the comparison view 118includes at least two listings for which the action user-interfacecomponents initiate different actions in relation to the respectivelistings.

In general, these different action user-interface components are userselectable to initiate the different actions in relation to respectivelistings. For instance, selection of the action user-interface component212 initiates a different action in relation to the listing 202 (e.g.,“Add to Cart”) than selection of the action user-interface component 214initiates in relation to the listing 204 (e.g., “Buy It Now”). Withrespect to e-commerce, the different actions may correspond to differentstages of conversion in relation to a respective listed item. Examplesof different e-commerce actions may include “Add to Cart,” “Buy It Now,”“Place Bid,” “Make Offer,” “Share,” and “Add to Watch List.”

Generally speaking, an “Add to Cart” action involves adding an item of alisting to an online shopping cart. A “Buy It Now” action can involvebuying an item of a listing at a time that a corresponding component(e.g., a “Buy It Now” button) is substantially selected, e.g., subjectto entry of additional information such as confirming credit cardinformation or in some cases no additional information. A “Place Bid”action can involve submitting a bid for an auction to buy an item of alisting. A “Make Offer” action can involve entering an offer to buy anitem of a listing. A “Share” action can involve sharing a listing, e.g.,via email, text message, other messaging application, one or more socialnetworks, one or more online forums, and so forth. Additionally oralternately, the “Share” action may involve sharing the comparison view118, such that more than one of the listings is shared (e.g., all of thelistings of the comparison view 118). An “Add to Watch List” action caninvolve adding an item of a listing to a watch list of items. Certainly,these are merely examples, and different e-commerce platforms mayoperate using different sets of actions that cause transitions todifferent stages without departing from the spirit or scope of thedescribed techniques.

Regardless, initiation of different actions results in transitions todifferent stages in relation to a respective listing, such that if adifferent action were initiated for a same listing, then the listingplatform 104 would cause a transition to a different stage in relationto the listing, e.g., by updating a user profile to reflect that alisted item has been added to a cart, added to a wish list, bid on(listing data for an item is also updated to reflect bids on the item),and so forth. This contrasts with the mere inclusion of a differentuniform resource locator (URL) for each listing in a comparison view,where selection of a listing's URL simply causes navigation to adedicated web page for the listing, e.g., a view item page for theparticular listed item. In a scenario where the comparison view 118 isused in connection with fantasy sports, the different actions maycorrespond to different stages of player management in relation to arespective listed player. Examples of different fantasy sports actionsmay include “Add to Watch List,” “Propose Trade,” “Pick Up fromWaivers,” “Add Free Agent,” “Bench,” “Drop,” selection of a startingroster spot, and so forth.

These different actions may be visually indicated in the comparison view118 in various ways. By way of example, the different user-interfacecomponents may include displayed text indicative of a respective actionthat is performed responsive to selection. In the illustrated example200, for instance, the action user-interface components 212, 214, 216,218, 220 are depicted as selectable buttons which each have textindicative of an action taken responsive to selection. Different actionuser-interface components may have a variety of differing visualcharacteristics—that enable users to distinguish between the differentactions initiated—without departing from the spirit or scope of thedescribed techniques. For instance, action user-interface components fordifferent actions may have different colors, different fonts, differentsizes, different shadowing, different three-dimensional (3D) effects(e.g., raised or sunken), different animations (e.g., responsive tohovering or holding a finger/stylus on the display of a component), andso on.

In addition, other user interface components may be output to informusers that the different action-interface components initiate differentactions and/or inform them about one or more effects of those actions.For example, the display module 112 may cause pop ups to be displayedthat describe the different actions initiated responsive to selection ofthe action user-interface components 212, 214, 216, 218, 220. Such popups may be displayed based on hovering or other similar user interactionproximate the action user-interface components 212, 214, 216, 218, 220.These pop ups may also be displayed a first time or a first few timesthe comparison view 118 is displayed to a user. Additionally oralternately, an output module of the computing device 102 may cause anaudible output describing the different actions initiated responsive toselection of the action user-interface components 212, 214, 216, 218,220. The described systems may inform users about different actionsinitiated responsive to selection of different action user-interfacecomponents in a variety of ways in accordance with the describedtechniques.

In addition to these action user-interface components, the comparisonview 118 also displays a plurality of attributes 222 for each of thelistings 202, 204, 206, 208, 210. Broadly speaking, the attributes 222included in the comparison view 118 enable the listings 202, 204, 206,208, 210 to be compared. In one or more scenarios, the attributes 222included in the comparison view 118 are predetermined for a category ofitems (e.g., mobile phones). Additionally or alternately, the attributes222 may be determined based on availability of information for thelistings selected for the comparison view 118, such that if all or somethreshold of the selected listings have information for a particularattribute (e.g., an image), then that attribute is included in thecomparison view. In the illustrated example 200, the attributes 222include an item image, a listing title (e.g., “Acme mobile—128GB—White”), a price, shipping information, a seller rating, returninformation, and item status as new or used. It is to be appreciatedthat the comparison view 118 may include different attributes withoutdeparting from the spirit or scope of the described techniques. Forinstance, when the comparison view 118 is used in connection withfantasy sports, the attributes 222 may include a player image, currentrank, projected rank, injury status, upcoming matchups, news, individualstatistics, and so forth.

The illustrated example 200 also includes a hand 224, e.g., of the user114. The hand 224 represents that the action user-interface components212, 214, 216, 218, 220 may be selectable based on touch input toinitiate a respective action. Although the hand 224 is depicted, theaction user-interface components 212, 214, 216, 218, 220 may be selectedin a variety of other ways, such as with a stylus, a cursor controlledby a mouse or track ball, gaze-based inputs, gestures, keyboard inputs,voice commands and so forth.

In this example 200, the comparison view 118 also includes comparisonexplanations 226, 228, 230, 232, 234. In one or more implementations,the comparison explanations 226, 228, 230, 232, 234 explain why arespective listing is included in the comparison view 118. Thecomparison explanation 226, for instance, explains that the listing 202is included in the comparison view 118 because it corresponds to an itemthe user 114 is currently viewing, which is discussed further inrelation to FIG. 3. The comparison explanations 228, 230, 232, 234explain that the respective listings 204, 206, 208, 210 are included inthe comparison view 118 because they are similar to the listing 202.Listings may be selected for inclusion in the comparison view 118 forother reasons and thus include different comparison explanations, suchas “In Cart,” “In Watch List,” and so on. The determination of whichlistings are selected for inclusion as part of the comparison view 118is discussed in further detail in relation to FIG. 4.

In the illustrated example 200, the comparison view 118 is depicted as astandalone user interface without other user interface components. Inone or more implementations, the comparison view 118 may indeed bedisplayed via the display device 116 without other user interfacecomponents, e.g., in a mobile application and responsive to selection ofan option, in a view item page, to show the comparison view 118. Inother implementations, though, the comparison view 118 may be includedas a component of digital content. For instance, the comparison view 118may be included as a component of a web page having additional digitalcontent. In this context, consider the following discussion of FIG. 3.

FIG. 3 depicts another example 300 of a user interface in which thecomparison view is included as a component of the user interface. Inparticular, the illustrated example 300 depicts user interface 302,which includes the comparison view 118 and listing specific portion 304.

Here, the listing specific portion 304 corresponds to the listing 202.In contrast to display of the listing 202 in the comparison view 118,the listing specific portion 304 provides more screen space to displayinformation and other digital content (e.g., image 306) of the listing202. For example, the listing specific portion 304 includes informationfrom the attributes 222 of the listing 202, e.g., listing title, price,shipping information, return information, and item status as new orused. As included in the listing specific portion 304, this informationmay include a greater amount of information than in the comparison view118. Consider the shipping information attribute 222, for example. Inthe comparison view 118, the listing 202 merely includes the indicationthat shipping is free. The listing specific portion 304 includesadditional, clarifying information relative to the comparison view 118.In particular, the listing specific portion 304 indicates that“Standard” shipping is free and includes a hyperlink to see moreshipping details. The listing specific portion 304 also includesadditional graphics to enhance the presentation of the listing 202 inthe listing specific portion 304, e.g., graphics of acceptable forms ofpayment.

Notably, the listing specific portion 304 also includes display ofmultiple, different action user-interface components 308. Thesemultiple, different action user-interface components 308 are selectableto initiate different actions in relation to the listing 202. Thesemultiple, different action user-interface components 308 may correspondto the actions that are allowed in relation to the listing 202, e.g.,all of the allowed actions. Some actions used by the listing platform104 may not be allowed for some listings, such as when a listing userselects a subset of the platform's actions to expose in connection witha respective listing or when only a subset of the platform's listingsare applicable to a manner in which the listing user selects to sell alisted item (e.g., auction versus fixed price). The inclusion of thesemultiple, different action user-interface components 308 in the listingspecific portion 304 further represents that the single actionuser-interface component 212 is included in the comparison view 118based on a determination of a single action from multiple actionsavailable in relation to the listing 202.

In this context, it is also noted that the comparison view 118 displaysonly a single action user-interface component in each of the listings202, 204, 206, 208, 210. Moreover, each of the action user-interfacecomponents 212, 214, 216, 218, 220 is positioned at a same relativelocation within a respective listing. In the illustrated examples, thelistings are arranged horizontally in the comparison view 118, onelisting to another, such that the action user-interface components arepositioned at a same vertical location within the respective listing. Ina scenario where listings are arranged vertically in the comparison view118, one listing to another, the action user-interface components may bepositioned at a same horizontal location within the respective listing.In one or more implementations, listings in the comparison view 118 mayeach include display of multiple action user-interface components. Byway of example, each listing may include two selectable buttons. In suchscenarios, the action user-interface components displayed in at leastone of the listings are different from the action user-interfacecomponents displayed in at least one other listing in the comparisonview 118. For instance, a first listing may include a display of an “Addto Cart” button and a “Buy It Now” button, and a second listing mayinclude a display of an “Add to Cart” button and a “Place Bid” button,but not the “Buy It Now” button. In other words, the listings maydisplay different combinations of multiple action user-interfacecomponents. In the context of determining which action of the listingplatform 104's available actions to expose via an action user-interfacecomponent in a listing of the comparison view 118, consider thefollowing discussion of FIG. 4.

FIG. 4 depicts an example 400 of an implementation in which thecomparison view system of FIG. 1 generates a comparison view withdifferent action user-interface components. From FIG. 1, the illustratedexample 400 includes the comparison view system 120.

In this example 400, the comparison view system 120 is depictedobtaining the listing data 130 and the user data 124 as input, e.g.,from the storage 132, 126. The comparison view system 120 is alsodepicted outputting the comparison view 118. The comparison view system120 may output the comparison view 118 by communicating it over thenetwork 108 to the computing device 102, enabling the display module 112to cause display of the comparison view 118 via the display device 116.In at least some implementations, the comparison view system 120 maycommunicate information, corresponding to the comparison view 118, whichenables the display module 112 to render the comparison view 118 fordisplay via the display device 116.

In this example 400, the comparison view system 120 is illustrated asincluding listing selection agent 402, feature selection and extractionengine 404, machine learning model 406, and comparison view generator408. These components are configured to perform various actions andgenerate various data as discussed above and below that is used toproduce the comparison view 118. It is to be appreciated that althoughthe comparison view system 120 is illustrated with these components, thecomparison view system 120 may include more, fewer, and/or differentcomponents to produce the comparison view 118 without departing from thespirit or scope of the described techniques.

Initially, the listing selection agent 402 selects a plurality oflistings to include in the comparison view 118. Selected listings 410represent the listings selected by the listing selection agent 402 fromthe listings 134 listed by the listing platform 104 and for inclusion inthe comparison view 118. The listing selection agent 402 may select theselected listings 410 in various ways. For example, the listingselection agent 402 may select similar listings for inclusion in thecomparison view 118, such as when the comparison view 118 is displayedas a component of a user interface (e.g., web page) for a particularlisting and the similar listings are selected as being similar to theparticular listing. In addition or alternately, the listing selectionagent 402 may select the selected listings 410 based on inclusion in anexisting list, such as the user 114's watch list (or wish list), basedon having been added to the user 114's online shopping cart, and soforth. In the context of e-commerce, the listing selection agent 402 mayselect the selected listings 410 based on a merchandising algorithm,such as a known merchandising algorithm used to select multiple listingsfor inclusion in comparison views.

The feature selection and extraction engine 404 identifies aspects of acontext in which the comparison view 118 is displayed. The featureselection and extraction engine 404 identifies these aspects to quantifythem for input to the machine learning model 406. The feature selectionand extraction engine 404 also extracts data describing the identifiedaspects from the listing data 130 and the user data 124. The featureselection and extraction engine 404 packages this extracted data as viewcontext representation 412. The feature selection and extraction engine404 may generate a view context representation 412 for each of theselected listings 410—one view context representation for one selectedlisting.

Broadly speaking, the view context representation 412 describes thecontext in which the comparison view 118 is displayed. In one or moreimplementations, the feature selection and extraction engine 404generates the view context representation 412 as a feature vector, whichincludes a feature for each of the identified aspects. In suchimplementations, the feature selection and extraction engine 404converts the data extracted for an aspect into a numericalrepresentation of the aspect, i.e., a value indicative of the aspect.This conversion can be referred to as “quantification.” The featureselection and extraction engine 404 then sets a value for the aspect'scorresponding feature to the numerical representation of the aspect. Thefeature selection and extraction engine 404 sets values for each featureof the feature vector to the numerical representation derived for thecorresponding aspect.

In one or more implementations, the feature selection and extractionengine 404 extracts from the listing data 130 and the user data 124 datadescribing context aspects including: the user 114's most used actionuser-interface component (e.g., most used button) leading to conversionin connection with previous listings (e.g., previously purchased items);a category associated with a listing for which the action is beingpredicted; price range or cost associated with the listing; number oftimes the listing is watched, added to cart, and/or viewed by the otherusers 122. In such implementations, the feature selection and extractionengine 404 thus derives a numerical representation of each of theseaspects from the listing data 130 and the user data 124 and incorporatesthem as features into a feature vector implementing the view contextrepresentation 412.

With respect to the example implementation aspects discussed above, thecategory of a listing affects the context in which the comparison view118 is displayed because users interact differently with listings indifferent categories. Accordingly, the category associated with alisting may affect how the user 114 behaves in relation to the listing.By way of example, users interact with listings for electronics (e.g.mobile devices) differently from listings for collectibles (e.g., comicbooks) and listings for auto parts (e.g., oil filters).

In a scenario where the listing platform 104 corresponds to ane-commerce platform, examples of categories that can be associated witha listing may include antiques; art; baby; books; business & industrial;cameras & photo; cell phones & accessories; clothing, shoes &accessories; coins & paper money; collectibles; computers/tablets &networking; consumer electronics; crafts; dolls & bears; DVDs & movies;automobile & motors; entertainment memorabilia; gift cards & coupons;health & beauty; home & garden; jewelry & watches; music; musicalinstruments & gear; pet supplies; pottery & glass; real estate;specialty services; sports memorabilia, cards & fan shop; stamps;tickets & experiences; toys & hobbies; travel; video game consoles; andso on. E-commerce platforms may use different categories withoutdeparting from the spirit or scope of the described techniques.Additionally, other types of listing platforms 104—such as fantasysports platforms, platforms for financial instrument trading, socialnetworking platforms, and online dating platforms—may have a variety ofdifferent categories without departing from the spirit or scope of thedescribed techniques.

In a similar manner as a listing's category, a price or other costassociated with a listing affects the context in which the comparisonview 118 is displayed because users interact differently with listingsin different price ranges or having otherwise different costs. Withrespect to prices, for instance, users generally interact with listingsthat list an item for $100.00 USD or less differently from listings thatlist an item at more than $1,000,000 USD. For example, users may moreoften select “Buy It Now” or “Add to Cart” in connection with listingsthat list items for $100.00 USD or less and may more often select “Addto Wish List” for listings that list an item for more than $1,000,000USD. To the extent that price affects the action user-interfacecomponents that users select to lead to conversions, the price aspectmay be captured in the view context representation 412, e.g., as afeature of a feature vector. In the fantasy sports scenario, the priceaspect may correspond to other costs to a user, such as loss of waiverpriority or loss of virtual currency. Although the above-noted aspectsdescribing the comparison view 118's context are discussed, it is to beappreciated that the view context representation 412 may be configuredto represent different aspects and combinations of aspects withoutdeparting from the spirit or scope of the described techniques.

Once generated, the comparison view system 120 provides the view contextrepresentation 412 (e.g., a feature vector) as input to the machinelearning model 406. Based on the view context representation 412, themachine learning model 406 generates listing action prediction 414. Tothe extent that a view context representation 412 is provided as inputfor each of the selected listings 410, the machine learning model 406generates a listing action prediction 414 for each of the selectedlistings 410. The listing action prediction 414 indicates whichavailable action is predicted most likely to cause conversion inrelation to the respective selected listing 410. In one or moreimplementations, the machine learning model 406 generates the listingaction prediction 414 as a feature vector, where each featurecorresponds to an available action for a selected listing. In suchimplementations, the value of each feature corresponds to a probabilitythat the user 114 will choose an interface component of the respectiveaction.

The action determined for a selected listing 410 may be determined, inpart, by processing the listing action prediction 414, configured as afeature vector, according to a probability density function. Thisprocessing may be carried out by the comparison view system 120 or oneof its components, e.g., the comparison view generator 408. Byprocessing the feature vector with this probability density function,the comparison view system 120 determines which action to associate withthe selected listing 410 in the comparison view 118. The comparison viewgenerator 408 generates the comparison view 118 to include in theselected listing 410 an action user-interface component that correspondsto the determined action. Although processing with the probabilityfunction may often yield determination of a most probable action giventhe discussed feature vector, this processing does not always yielddetermination of the most probable action.

In one or more implementations, the machine learning model 406 is areinforcement learning model. Initially, this reinforcement learningmodel may be configured to generate the listing action prediction 414according to an initial, hardcoded policy, such as a policy thatinstructs the model to determine the action “Buy It Now” for a firstlisting in the comparison view 118, determine the action “Make Offer”for a second listing in the comparison view 118, and so on. Over time,the machine learning model 406 refines this policy. The policy isrefined based on the actions determined for the selected listings 410,monitored user interactions with action user-interface componentsdisplayed based on the determined actions over a time period subsequentto the display (e.g., 24 hours), and by negatively or positivelyreinforcing the reinforcement learning model depending on the monitoreduser interactions over this time period.

If the user 114 selects an action user-interface component within thesubsequent time period, for example, then the reinforcement learningmodel is positively reinforced for predicting the respective action,e.g., the model is positively reinforced by associating the predictionwith a positive reward of +1. On the other hand, if the user 114 doesnot select the action user-interface component within the subsequenttime period, then the reinforcement learning model is negativelyreinforced (or “punished”) for predicting the respective action, e.g.,the model is negatively reinforced by associating the prediction with anegative reward of −1. Based on these rewards, the comparison viewsystem 120 adjusts internal weights of the reinforcement learning model(the machine learning model 406). By adjusting these internal weights,the policy embodied by the machine learning model 406 is updated. Theseadjustments are made to optimize the model for some pre-selected goal,such as to maximize a number of users that make a purchase via thelisting platform 104 or maximize a purchase volume across all users ofthe listing platform 104. The model may also be optimized based on aweighted combination of goals, such as a weighted combination of numberof users making purchases and purchase volume. Through reinforcement ofits decisions (e.g., by association of positive or negative rewards withpredictions), the reinforcement learning model learns what actions topredict based on previous predictions made for the user 114 and for theother users 122.

By configuring the machine learning model 406 as a reinforcementlearning model, the machine learning model 406 is able to learn userbehavior over time and is adjusted to account for changes in userbehavior rather than treating users as static. Further, thereinforcement learning model is provided input for each predictiondescribing the context of displaying the comparison view 118 inreal-time and at a particular time the comparison view 118 is requestedfor display—this accounts for changes in behaviors of individual usersas well as of groups of users (e.g., due to market conditions). Thereinforcement learning model may also be updated online (e.g., inreal-time as feedback is received), or may be updated in batch (e.g.,once a day based on the feedback received over the day).

When implemented as a reinforcement learning model, the machine learningmodel 406 may generate listing action predictions 414 for the same user114 that are different at a first time than at a second time for thesame selected listings 410. The machine learning model 406 may alsogenerate listing action predictions 414 at a same time for differentusers that are different for the same selected listings 410. This is dueto differing contexts for a given user at different times (e.g., thegiven user may or may not have interacted with components causingcontext to change at a subsequent time) and differing contexts fordifferent users at a same time. Through implementation as areinforcement learning model, the machine learning model 406 contrastswith classifiers and neural networks because the machine learning model406 learns a policy for predicting actions during actual deploymentrather than through a training process using one or more training datasets.

FIG. 5 depicts a different example 500 of a user interface displayingthe comparison view and having multiple listings with different actionuser-interface components. The illustrated example 500 depicts adifferent configuration of the comparison view 118 than FIGS. 2 and 3,but also includes multiple listings with different action user-interfacecomponents.

In this example 500, the comparison view includes listings 502, 504,506, 508, 510, which each include an action user-interface component512, 514, 516, 518, 520, respectively. Here, the action user-interfacecomponents 512, 514, 516, 518, 520 are selectable to initiate theactions “Add to Cart,” “Buy It Now,” “Place Bid,” “Make Offer,” and“Share,” respectively, and which are discussed in more detail above. Asalso noted above, though, user-interface components of the comparisonview 118 may be selectable to initiate different actions withoutdeparting from the spirit or scope of the described techniques.

In contrast to the comparison view 118 as illustrated in FIGS. 2 and 3,the comparison view 118 as illustrated in FIG. 5 has some differentcomponents with which a user—represented by hand 522—can interact andprovide different functionality and information from the previouslydiscussed components. It is to be noted that various combinations of thecomponents discussed in relation to FIG. 5 as well as in relation toFIGS. 2 and 3 may be included in the comparison view 118 in one or moreimplementations. It is also to be appreciated that some of thecomponents discussed in relation to these figures may not be includedone or more implementations.

In any case, the different components depicted in FIG. 5 include, foreach listing, an editing option 524 associated with the actionuser-interface components, a discount attribute 526, and a commentsfield 528. In one or more implementations, the editing option 524 isselectable to edit aspects of a respective listing or actionuser-interface component. By way of example, selection of the editingoption 524 can surface one or more further options which enable the userto change an action initiated responsive to selection of the respectiveaction user-interface component, e.g., change an action initiated from“Add to Cart” to “Share”. Responsive to interaction with these options,the comparison view system 120 can also change a visual appearance ofthe respective action user-interface component to indicate the actioninitiated, such as by changing text displayed on a corresponding buttonor any of the various other visual characteristics discussed above.

As noted above, a tracking service tracks interactions of users withinterfaces of the listing platform 104. This includes trackinginteractions of users with the editing option 524 to change an actioninitiated due to selection of an action user-interface component, e.g.,interactions to change an “Add to Watch List” button to a “Buy It Now”button. To this end, the user data 124, namely, the tracked interactions128 that are provided as input to the comparison view system 120, can beused to further refine the policy implemented by the machine learningmodel 406. For instance, user interaction to change an actionuser-interface element may be captured via the view contextrepresentation 414, e.g., as one or more features of a vectorrepresentation.

Additionally, the machine learning model 406 may be positively ornegatively reinforced as discussed above based on this interaction, suchas by rewarding the machine learning model 406 negatively for predictinga first action rather than a second action to which the user changed anaction user-interface component. Indeed, these interactions of users tochange the action user-interface components may be captured andaccounted for through reinforcement (e.g., rewards) to the machinelearning model 406. This may be handled in scenarios where the machinelearning model 406 is configured as a reinforcement learning model, suchas by using a process as discussed in more detail above in relation tothe machine learning model 406 being configured as a reinforcementlearning model.

It is to be appreciated though that interaction of a user to change anaction user-interface component may be captured in connection with othermachine learning models, and also used to inform subsequent predictionsregarding which actions to initiate for each action user-interfacecomponent of the comparison view 118, in various ways without departingfrom the spirit or scope of the described techniques.

In addition to enabling a user to change an action initiated, selectionof the editing option 524 can also cause options to be surfaced forremoving a listing from the comparison view 118, reporting a listing(e.g., for including information or listing an item a user believesviolates one or more policies of the listing platform 104), adding logicin relation to a respective listing, and so forth. The term “addinglogic” refers to the ability of the user to specify how the listingplatform 104 is to process a given listing based on occurrence of one ormore conditions. This “logic” may be specified in the form of “if/then”conditions, for example. Consider an example in which a user places abid for an auction of a first listing of the listing platform. In thisexample, the “Add Logic” option can be selected to enable the user tospecify that “if” the user does not win the auction for the firstlisting with the placed bid, “then” the listing platform 104 is simplyto buy a second listing at a time the auction for the first listing isnot won, e.g., equivalent to selection of “Buy It Now” in relation tothe second listing. In the context of fantasy sports, for instance, thiscan enable a user to specify logic to pick up a secondary player as afree agent if a primary player does not clear the league's waiver wire.In one or more implementations, the listing platform 104 may enable auser to specify multiple if/then statements and/or other logic forprocessing listings, e.g., if/else, for each, nested logic, and soforth.

In the context of the illustrated example 500, it represents a scenariowhere the hand 522 of the user selects the editing option 524 of thelisting 506. Responsive to this selection, various editing options aredisplayed in connection with the listing. Specifically, and as discussedabove, the options of “Edit Button,” “Remove From View,” “ReportListing,” “Add Logic,” and “Cancel” are depicted. It is to beappreciated that selection of the editing option 524 may surfacedifferent options without departing from the spirit or scope of thedescribed techniques and also that the editing option 524 may beconfigured differently (e.g., have a different appearance or bepositioned differently within a listing) in implementations.

The discount attribute 526 indicates discounts available in relation toa respective listing. For example, the discount attribute 526 canindicate whether any discounts are available in relation to therespective listing or not. If any discount is available, the discountattribute 526 may provide information about one or more of thediscounts. By way of example, the discount attribute 526 may specify anamount off of a respective listing, an amount off if a particular actionis initiated by a user in relation to the respective listing (e.g., “BuyIt Now,” “Share,” etc.), an amount off if a user purchases asupplemental item along with an item listed in a respective listing, asubscription discount, a buy one get one free discount, and so forth.The discount attribute 526 may be used to specify a variety of discountsin relation to a listing without departing from the spirit or scope ofthe described techniques.

The comments field 528 enables a user to specify information in relationto a respective listing and also displays that information after it isinput. For instance, the comments field 528 enables a user to inputlists of pros and cons in relation to listings of the comparison view118. The comments field 528 may persist this information across multiplesessions of interaction with the listing platform 104, cause theinformation to be saved to a user profile, cause the information to besaved using a cookie, or maintained simply while viewing the comparisonview 118 and then deleted or otherwise not maintained. The commentsfield 528 enables a user to specify a variety of information (e.g., bytyping or voice input) for display in the field, and also enables a userto edit the specified information in relation to a given listing withoutdeparting from the spirit or scope of the described techniques.

Having discussed example details of the techniques for different actionuser-interface components in a comparison view, consider now someexample procedures to illustrate additional aspects of the techniques.

Example Procedures

This section describes example procedures for different actionuser-interface components in a comparison view in one or moreimplementations. Aspects of the procedures may be implemented inhardware, firmware, or software, or a combination thereof. Theprocedures are shown as a set of blocks that specify operationsperformed by one or more devices and are not necessarily limited to theorders shown for performing the operations by the respective blocks. Inat least some implementations the procedures are performed by a suitablyconfigured device, such as the computing device 102 of FIG. 1 having adisplay module 112 or the listing platform 104 having the comparisonview system 120.

FIG. 6 depicts an example procedure 600 in which in which a comparisonview is displayed with different action user-interface components.

A plurality of listings is displayed concurrently in a comparison viewvia a user interface of a listing platform (block 602). By way ofexample, the display module 112 causes display of the comparison view118 via the display device 116 of the computing device 102. As discussedabove and below, the comparison view 118 includes multiple listings,such as the listings 202, 204, 206, 208, 210 depicted in FIG. 2.

An action user-interface component is displayed in each of the pluralityof listings (block 604). In accordance with the principle discussedherein, the action user-interface components displayed in at least twoof the plurality of listings, responsive to selection, initiatedifferent actions in relation to a respective listing. By way ofexample, the display module 112 causes display of the actionuser-interface components 212, 214, 216, 218, 220 in the listings 202,204, 206, 208, 210, respectively. As discussed in relation to FIG. 2, atleast two of these action user-interface components 212, 214, 216, 218,220 initiate different actions responsive to selection and in relationto their respective listing. Selection of the action user-interfacecomponent 212, for instance, initiates an action (“Add to Cart”) inrelation to the listing 202 that is different from an action (“Buy ItNow) initiated in relation to the listing 204 responsive to selection ofthe action user-interface component 214.

FIG. 7 depicts an example procedure 700 in which a comparison view isgenerated, based on user data and using machine learning, to includedifferent action user-interface components.

A selection to display a comparison view is received (block 702). By wayof example, the computing device 102 receives a selection to navigate tothe user interface 302 having the listing specific portion 304 and thecomparison view 118. This selection may correspond to selection of a URLto navigate to a web page configured as the user interface 302, a searchresult provided responsive to a search query, and so forth.

A plurality of listings is selected for inclusion in the comparison view(block 704). By way of example, the listing selection agent 402 selectsthe selected listings 410 from the available listings 134 listed by thelisting platform 104. For instance, the listing selection agent 402selects the listings according to one or more merchandising algorithms.

The following steps of blocks 706-710 are repeated for each individuallisting selected for inclusion in the comparison view 118. By way ofexample, if there are three listings selected for inclusion in thecomparison view 118, then the blocks 706-708 are repeated three times,once in relation to each of these listings. Features of listing data anduser data are selected for extraction in connection with generating aview context representation for a respective listing and as input to amachine learning model (block 706). By way of example, the featureselection and extraction engine 404 selects aspects of a context, inwhich the comparison view 118 is displayed, to quantify for input to themachine learning model 406. In this example, the feature selection andextraction engine 404 extracts data corresponding to the identifiedfeatures from the listing data 130 and the user data 124. The featureselection and extraction engine 404 generates the view contextrepresentation 412 to persist the extracted data. When the view contextrepresentation 412 is configured as a feature vector, for instance, theextracted data is persisted as numerical representations in features ofthis feature vector.

The view context representation is provided as input to the machinelearning model (block 708). By way of example, the comparison viewsystem 120 provides the view context representation 412 as input to themachine learning model 406. A prediction is obtained as output from themachine learning model (block 710). In accordance with the principlesdiscussed herein, the prediction predicts an action of a plurality ofactions to associate with the respective listing. By way of example, thecomparison view system 120 obtains the listing action prediction 414 asoutput from the machine learning model 406. As discussed above, thelisting action prediction 414 is usable—with application of aprobability density function—to predict an action of the listingplatform 104's actions to associate with the respective selected listing410.

The comparison view is generated to include action user-interfacecomponents based on predictions for each of the plurality of listingsthat is to be included in the comparison view (block 712). By way ofexample, the comparison view generator 408 generates the comparison view118 to include action user-interface components based on the predictionsobtained at block 710 for all of the selected listings 410.

The comparison view is provided to a user corresponding to the selection(block 714). By way of example, the comparison view 118 is communicatedover the network to the computing device 102 and the display module 112causes display of the comparison view 118 via the display device 116.Although the comparison view system 120 is depicted as being separatefrom the computing device 102, the comparison view system 120 may beincluded in the computing device 102 in one or more implementations,e.g., as part of one of the applications 110. In such scenarios, thecomparison view 118 may be retrieved by the display module 112 frommemory (not shown) and displayed via the display device 116.

Having described example procedures in accordance with one or moreimplementations, consider now an example system and device that can beutilized to implement the various techniques described herein.

Example System and Device

FIG. 8 illustrates an example system generally at 800 that includes anexample computing device 802 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe display module 112 and the comparison view system 120. The computingdevice 802 may be, for example, a server of a service provider, a deviceassociated with a client (e.g., a client device), an on-chip system,and/or any other suitable computing device or computing system.

The example computing device 802 as illustrated includes a processingsystem 804, one or more computer-readable media 806, and one or more I/Ointerfaces 808 that are communicatively coupled, one to another.Although not shown, the computing device 802 may further include asystem bus or other data and command transfer system that couples thevarious components, one to another. A system bus can include any one orcombination of different bus structures, such as a memory bus or memorycontroller, a peripheral bus, a universal serial bus, and/or a processoror local bus that utilizes any of a variety of bus architectures. Avariety of other examples are also contemplated, such as control anddata lines.

The processing system 804 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 804 is illustrated as including hardware elements 810 that may beconfigured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 810 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable storage media 806 is illustrated as includingmemory/storage 812. The memory/storage 812 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 812 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 812 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable media 806 may be configured in a variety of other waysas further described below.

Input/output interface(s) 808 are representative of functionality toallow a user to enter commands and information to computing device 802,and also allow information to be presented to the user and/or othercomponents or devices using various input/output devices. Examples ofinput devices include a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, touch functionality (e.g., capacitiveor other sensors that are configured to detect physical touch), a camera(e.g., which may employ visible or non-visible wavelengths such asinfrared frequencies to recognize movement as gestures that do notinvolve touch), and so forth. Examples of output devices include adisplay device (e.g., a monitor or projector), speakers, a printer, anetwork card, tactile-response device, and so forth. Thus, the computingdevice 802 may be configured in a variety of ways as further describedbelow to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the computing device 802. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices thatenable persistent and/or non-transitory storage of information incontrast to mere signal transmission, carrier waves, or signals per se.Thus, computer-readable storage media refers to non-signal bearingmedia. The computer-readable storage media includes hardware such asvolatile and non-volatile, removable and non-removable media and/orstorage devices implemented in a method or technology suitable forstorage of information such as computer readable instructions, datastructures, program modules, logic elements/circuits, or other data.Examples of computer-readable storage media may include, but are notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, harddisks, magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing mediumthat is configured to transmit instructions to the hardware of thecomputing device 802, such as via a network. Signal media typically mayembody computer readable instructions, data structures, program modules,or other data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 810 and computer-readablemedia 806 are representative of modules, programmable device logicand/or fixed device logic implemented in a hardware form that may beemployed in some embodiments to implement at least some aspects of thetechniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 810. The computing device 802 may beconfigured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the computing device802 as software may be achieved at least partially in hardware, e.g.,through use of computer-readable storage media and/or hardware elements810 of the processing system 804. The instructions and/or functions maybe executable/operable by one or more articles of manufacture (forexample, one or more computing devices 802 and/or processing systems804) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 802 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 814 via a platform 816 as describedbelow.

The cloud 814 includes and/or is representative of a platform 816 forresources 818. The platform 816 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 814. Theresources 818 may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe computing device 802. Resources 818 can also include servicesprovided over the Internet and/or through a subscriber network, such asa cellular or Wi-Fi network.

The platform 816 may abstract resources and functions to connect thecomputing device 802 with other computing devices. The platform 816 mayalso serve to abstract scaling of resources to provide a correspondinglevel of scale to encountered demand for the resources 818 that areimplemented via the platform 816. Accordingly, in an interconnecteddevice embodiment, implementation of functionality described herein maybe distributed throughout the system 800. For example, the functionalitymay be implemented in part on the computing device 802 as well as viathe platform 816 that abstracts the functionality of the cloud 814.

Conclusion

Although the invention has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or acts described. Rather, the specificfeatures and acts are disclosed as example forms of implementing theclaimed invention.

What is claimed is:
 1. A method implemented at least partially inhardware of a computing device, the method comprising: displaying, via auser interface of a listing platform, an initial set of actionuser-interface components that are selectable to initiate actions for aninitial set of listings, the actions determined for the initial set oflistings according to an initial policy for determining the actions;refining the policy, using a machine learning model, based on userinteractions with the initial set of action user-interface componentsmonitored over a time period; displaying, via the user interface of thelisting platform, a plurality of listings concurrently in a comparisonview, the plurality of listings including at least a first listingrepresenting a first item and a second listing representing a seconditem; determining which action of a plurality of actions to associatewith each listing of the plurality of listings using the refined policy;and displaying, in each listing of the plurality of listings in thecomparison view and concurrently, only one respective actionuser-interface component including: a first respective actionuser-interface component selectable via the user interface to initiate afirst action with respect to the first item; and a second respectiveaction user-interface component selectable via the user interface toinitiate a second action with respect to the second item, the secondaction being different from the first action.
 2. The method as describedin claim 1, wherein the one respective action user-interface componentis displayed in a same relative position in each listing of theplurality of listings.
 3. The method as described in claim 1, whereinselection of the first respective action user-interface componentdisplayed in the first listing initiates one of: buying the first itemat a time of the selection; adding the first item to a cart; submittinga bid for an auction of the first item; entering an offer to buy thefirst item; sharing the first item; or adding the first item to a watchlist of items.
 4. The method as described in claim 3, wherein selectionof the second action user-interface component displayed in the secondlisting initiates a different one of: buying the second item at a timeof the selection; adding the second item to the cart; submitting a bidfor an auction of the second item; entering an offer to buy the seconditem; sharing the second item; or adding the second item to the watchlist of items.
 5. The method as described in claim 1, wherein eachlisting of the plurality of listings includes a set of attributes forcomparison to the set of attributes of each other listing.
 6. The methodas described in claim 1, wherein the first respective actionuser-interface component is displayed in a first column of thecomparison view that corresponds to the first listing and the secondrespective action user-interface component is displayed in a secondcolumn of the comparison view that corresponds to the second listing. 7.The method as described in claim 1, wherein the first respective actionuser-interface component includes text indicative of the first actionand the second action user-interface component includes different textindicative of the second action.
 8. The method as described in claim 1,wherein refining the policy includes positively reinforcing the policybased on user input being received during the time period to select anaction user-interface component of the initial set of actionuser-interface components.
 9. The method as described in claim 1,wherein refining the policy includes negatively reinforcing the policybased on user input not being received during the time period to selectan action user-interface component of the initial set of actionuser-interface components.
 10. The method as described in claim 1,further comprising: receiving user input to change the one respectiveaction user-interface component displayed in at least one listing of theplurality of listings; and further refining the policy based on the userinput.
 11. A system comprising: a display device; and a display moduleimplemented at least partially in software of a computing device andcommunicably coupled to the display device to: cause display, via thedisplay device, of an initial set of action user-interface componentsthat are selectable to initiate actions for an initial set of listings,the actions determined for the initial set of listings according to aninitial policy for determining the actions, the policy being refined bya machine learning model based on user interactions with the initial setof action user-interface components monitored over a time period; causedisplay, via the display device, of a plurality of listings concurrentlyin a comparison view, the plurality of listings including at least afirst listing that represents a first item and a second listing thatrepresents a second item, each listing of the plurality of listingsbeing associated with an action of a plurality of actions, the actiondetermined using the refined policy; and cause display, via the displaydevice and in each listing of the plurality of listings concurrently, ofonly one respective action user-interface component including: a firstrespective action user-interface component selectable via a userinterface of a listing platform to initiate a first action with respectto the first item; and a second respective action user-interfacecomponent selectable via the user interface to initiate a second actionwith respect to the second item, the second action being different fromthe first action.
 12. The system as described in claim 11, wherein thefirst respective action user-interface component displayed in the firstlisting has at least one different visual characteristic from the secondrespective action user-interface component displayed in the secondlisting.
 13. The system as described in claim 12, wherein the at leastone different visual characteristic is text, indicative of an actioninitiated responsive to selection of the one respective actionuser-interface component.
 14. The system as described in claim 12,wherein the at least one different visual characteristic is a color ofthe one respective action user-interface component.
 15. The system asdescribed in claim 11, wherein the one respective action user-interfacecomponent is configured as a selectable button in each listing of theplurality of listings.
 16. The system as described in claim 11, whereinthe one respective action user-interface component is displayed in asame relative position in each listing of the plurality of listings. 17.The system as described in claim 11, wherein each listing of theplurality of listings includes a set of attributes for comparison to theset of attributes of each other listing.
 18. A method implemented atleast partially in hardware of a computing device, the methodcomprising: displaying, via a user interface of a listing platform, aninitial set of action user-interface components that are selectable toinitiate actions for an initial set of listings, the actions determinedfor the initial set of listings according to an initial policy fordetermining the actions, the policy being refined by a machine learningmodel based on user interactions with the initial set of actionuser-interface components monitored over a time period; displaying, viathe user interface of the listing platform, a plurality of listingsconcurrently in a comparison view, the plurality of listings includingat least a first listing representing a first item and a second listingrepresenting a second item; determining which action of a plurality ofactions, available for use in connection with the listing platform, toassociate with each listing of the plurality of listings using therefined policy; and displaying, at a same relative position within eachlisting of the plurality of listings in the comparison view andconcurrently, only one respective action user-interface componentincluding: a first respective action user-interface component selectablevia the user interface to initiate a first action with respect to thefirst item; and a second respective action user-interface componentselectable via the user interface to initiate a second action withrespect to the second item, the second action being different from thefirst action.
 19. The method as described in claim 18, wherein themachine learning model is a reinforcement model that determines whichaction of the plurality of actions to associate with each listing of theplurality of listings based on at least one of: a most used actionuser-interface component that has led to conversion previously withlistings of the listing platform; a category associated with a listing;a price range associated with the listing; a number of times the listingis added to a watch list by users of the listing platform; a number oftimes the listing is added to an online shopping cart by the users ofthe listing platform; or a number of times the listing is viewed by theusers of the listing platform.
 20. The method as described in claim 18,further comprising determining which listings of the listing platform toinclude in the plurality of listings based on at least one of inclusionon a list of listings or similarity with a particular listing of theplurality of listings.